6th International Conference on Computational Intelligence in Music, Sound, Art and Design.

News

For the 20th year anniversary of the evo* conference, a website was made available with all the
information on the evoMUSART papers since 2003. The idea is to bring together all the publications
in a handy web page that allows the visitors to navigate through all papers, best papers, authors,
keywords, and years of the conference, while providing quick access to the Springer’s web page
links. Feel free to browse, search and bookmark: http://evomusart-index.dei.uc.pt/.

About EvoMUSART

Following the success of previous events and the importance of the field of computational intelligence, specifically, evolutionary and biologically inspired (artificial neural network, swarm, alife) music, sound, art and design, evoMUSART has become an evo* conference with independent proceedings since 2012. Thus, evoMUSART 2017 is the sixth International Conference on Computational Intelligence in Music, Sound, Art and Design.

The use of Computational Intelligence for the development of artistic systems is a recent, exciting and significant area of research. There is a growing interest in the application of these techniques in fields such as: visual art and music generation, analysis, and interpretation; sound synthesis; architecture; video; poetry; design; and other creative tasks.

The main goal of evoMUSART 2017 is to bring together researchers who are using Computational Intelligence techniques for artistic tasks, providing the opportunity to promote, present and discuss ongoing work in the area.

The event will be held in April, 2017 in Amsterdam, The Netherlands, as part of the evo* event.

Topics of interest

Submissions should concern the use of which use of Computational Intelligence techniques (e.g. Evolutionary Computation, Artificial Life, Machine Learning, Swarm Intelligence) in the generation, analysis and interpretation of art, music, design, architecture and other artistic fields. Topics of interest include, but are not limited to:
Generation

EvoMUSART abstracts

Title: Algorithmic Songwriting with ALYSIA
Authors: Margareta Ackerman and David Loker
Abstract: This paper introduces ALYSIA: Automated LYrical SongwrIting Application.
ALYSIA is based on a machine learning model using Random Forests, and we
discuss its success at pitch and rhythm prediction. Next, we show how ALYSIA was
used to create original pop songs that were subsequently recorded and produced.
Finally, we discuss our vision for the future of Automated Songwriting for both co-
creative and autonomous systems.

Title: On Symmetry, Aesthetics and Quantifying Symmetrical Complexity
Authors: Mohammad Majid al-Rifaie, Anna Ursyn, Robert Zimmer, and Mohammad
Ali Javaheri Javid
Abstract: The concepts of order and complexity and their quantitative evaluation have
been at the core of computational notion of aesthetics. One of the major challenges is
conforming human intuitive perception and what we perceive as aesthetically pleasing
with the output of a computational model. Informational theories of aesthetics have
taken advantage of entropy in measuring order and complexity of stimuli in relation to
their aesthetic value. However entropy fails to discriminate structurally different
patterns in a 2D plane. In this work, following an overview on symmetry and its
significance in the domain of aesthetics, a nature-inspired, swarm intelligence
technique (Dispersive Flies Optimisation or DFO) is introduced and then adapted to
detect symmetries and quantify symmetrical complexities in images. The 252
Jacobsen & Hˆfel's images used in this paper are created by researchers in the
psychology and visual domain as part of an experimental study on human aesthetic
perception. Some of the images are symmetrical and some are asymmetrical, all
varying in terms of their aesthetics, which are ranked by humans. The results of the
presented nature-inspired algorithm is then compared to what humans in the study
aesthetically appreciated and ranked. Whilst the authors believe there is still a long
way to have a strong correlation between a computational model of complexity and
human appreciation, the results of the comparison are promising.

Title: Towards Polyphony Reconstruction Using Multidimensional Multiple Sequence
Alignment
Authors: Dimitrios Bountouridis,Frans Wiering, Dan Brown, Remco C. Veltkamp
Abstract: The digitization of printed music scores through the process of optical music
recognition is imperfect. In polyphonic scores, with two or more simultaneous voices,
errors of duration or position can lead to badly aligned and inharmonious digital
transcriptions. We adapt biological sequence analysis tools as a post-processing step
to correct the alignment of voices. Our multiple sequence alignment approach works
on multiple musical dimensions and we investigate the contribution of each dimension
to the correct alignment. Structural information, such musical phrase boundaries, is of
major importance; therefore, we propose the use of the popular bioinformatics aligner
Mafft which can incorporate such information while being robust to temporal noise.
Our experiments show that a harmony-aware Mafft outperforms sophisticated,
multidimensional alignment approaches and can achieve near-perfect polyphony
reconstruction.

Title: Melody Retrieval and Classification Using Biologically-Inspired Techniques
Authors: Dimitrios Bountouridis, Dan Brown, Hendrik Vincent Koops, Frans Wiering,
Remco C. Veltkamp
Abstract: Retrieval and classification are at the center of Music Information Retrieval
research. Both tasks rely on a method to assess the similarity between two music
documents. In the context of symbolically encoded melodies, pairwise alignment via
dynamic programming has been the most widely used method. However, this
approach fails to scale-up well in terms of time complexity and insufficiently models
the variance between melodies of the same class. Compact representations and
indexing techniques that capture the salient and robust properties of music content,
are increasingly important. We adapt two existing bioinformatics tools to improve the
melody retrieval and classification tasks. On two datasets of folk tunes and cover song
melodies, we apply the extremely fast indexing method of the Basic Local Alignment
Search Tool (BLAST) and achieve comparable classification performance to
exhaustive approaches. We increase retrieval performance and efficiency by using
multiple sequence alignment algorithms for locating variation patterns and profile
hidden Markov models for incorporating those patterns into a similarity model.

Title: Evolved Aesthetic Analogies to Improve Artistic Experience
Authors: Aidan Breen, Colm O'Riordan, and Jerome Sheahan
Abstract: It has been demonstrated that computational evolution can be utilised in the
creation of aesthetic analogies between two artistic domains by the use of mapping
expressions. When given an artistic input these mapping expressions can be used to
guide the generation of content in a separate domain. For example, a piece of music
can be used to create an analogous visual display. In this paper we examine the
implementation and performance of such a system. We explore the practical
implementation of real-time evaluation of evolved mapping expressions, possible
musical input and visual output approaches, and the challenges faced therein. We
also present the results of an exploratory study testing the hypothesis that an evolved
mapping expression between the measurable attributes of musical and visual
harmony will produce an improved aesthetic experience compared to a random
mapping expression. Expressions of various fitness values were used and the
participants were surveyed on their enjoyment, interest, and fatigue. The results of this
study indicate that further work is necessary to produce a strong aesthetic response.
Finally, we present possible approaches to improve the performance and artistic merit
of the system.

Title: Deep Artificial Composer: A Creative Neural Network Model for Automated
Melody Generation
Authors: Florian Colombo, Alexander Seeholzer, and Wulfram Gerstner
Abstract: The inherent complexity and structure on long timescales make the
automated composition of music a challenging problem. Here we present the Deep
Artificial Composer (DAC), a recurrent neural network model of note transitions for the
automated composition of melodies. Our model can be trained to produce melodies
with compositional structures extracted from large datasets of diverse styles of music,
which we exemplify here on a corpus of Irish folk and Klezmer melodies. We assess
the creativity of DAC-generated melodies by a new measure, the novelty of musical
sequences, showing that melodies imagined by the DAC are as novel as melodies
produced by human composers. We further use the novelty measure to show that the
DAC creates melodies musically consistent with either of the musical styles it was
trained on. This makes the DAC a promising candidate for the automated composition
of convincing musical pieces of any provided style.

Title: A Kind of Bio-inspired Learning of mUsic stylE
Authors: Roberto De Prisco, Delfina Malandrino, Gianluca Zaccagnino, Rocco
Zaccagnino and Rosalba Zizza
Abstract: In the field of Computer Music, computational intelligence approaches are
very relevant for music information retrieval applications. A challenging task in this
area is the automatic recognition of musical styles. The style of a music performer is
the result of the combination of several factors such as experience, personality,
preferences, especially in music genres where the improvisation plays an important
role. In this paper we propose a new approach for both recognition and automatic
composition of music of a specific performer's style. Such a system exploits: (1) a
one-class machine learning classifier to learn a specific music performer's style, (2) a
music splicing system to compose melodic lines in the learned style, and (3) a LSTM
network to predict patterns coherent with the learned style and used to guide the
splicing system during the composition. To assess the effectiveness of our system we
performed several tests using transcriptions of solos of popular Jazz musicians.
Specifically, with regard to the recognition process, tests were performed to analyze
the capability of the system to recognize a style. Also, we show that performances of
our classifier are comparable to that of traditional two-class SVM, and that it is able to
achieve an accuracy of 97%. With regard to the composition process, tests were
performed to verify whether the produced melodies were able to catch the most
significant music aspects of the learned style.

Title: Using autonomous agents to improvise music compositions in real-time
Authors: Patrick Hutchings and Jon McCormack
Abstract: This paper outlines an approach to real-time music generation using
melody and harmony focused agents in a process inspired by jazz improvisation. A
harmony agent employs a Long Short-Term Memory (LSTM) artificial neural network
trained on the chord progressions of 2986 jazz 'standard' compositions using a
network structure novel to chord sequence analysis. The melody agent uses a rule-
based system of manipulating provided, pre-composed melodies to improvise new
themes and variations. The agents take turns in leading the direction of the
composition based on a rating system that rewards harmonic consistency and melodic
flow. In developing the multi-agent system it was found that implementing embedded
spaces in the LSTM encoding process resulted in significant improvements to chord
sequence learning.

Title: Generating Polyphonic Music Using Tied Parallel Networks
Authors: Daniel D. Johnson
Abstract: We describe a neural network architecture which enables prediction and
composition of polyphonic music in a manner that preserves translation invariance of
the dataset. Specifically, we demonstrate training a probabilistic model of polyphonic
music using a set of parallel, tied-weight recurrent networks, inspired by the structure
of convolutional neural networks. This model is designed to be invariant to
transpositions, but otherwise is intentionally given minimal information about the
musical domain, and tasked with discovering patterns present in the source dataset.
We present two versions of the model, denoted TP-LSTM-NADE and BALSTM, and
also give methods for training the network and for generating novel music. This
approach attains high performance at a musical prediction task and successfully
creates note sequences which possess measure-level musical structure.

Title: Mixed-initiative Creative Drawing with webIconoscope
Authors: Antonios Liapis
Abstract: This paper presents the webIcononscope tool for creative drawing, which
allows users to draw simple icons composed of basic shapes and colors in order to
represent abstract semantic concepts. The goal of this creative exercise is to create
icons that are ambiguous enough to confuse other people attempting to guess which
concept they represent. webIcononscope is available online and all creations can be
browsed, rated and voted on by anyone; this democratizes the creative process and
increases the motivation for creating both appealing and ambiguous icons. To
complement the creativity of the human users attempting to create novel icons,
several computational assistants provide suggestions which alter what the user is
currently drawing based on certain criteria such as typicality and novelty. This paper
reports trends in the creations of webIcononscope users, based also on feedback
from an online audience.

Title: Clustering Agents for the Evolution of Autonomous Musical Fitness
Authors: Roisin Loughran and Michael O’Neill
Abstract: This paper presents a cyclical system that generates autonomous fitness
functions or Agents for evolving short melodies. A grammar is employed to create a
corpus of melodies, each of which is composed of a number of segments. A
population of Agents are evolved to give numerical judgements on the melodies
based on the spacing of these segments. The fitness of an individual Agent is
calculated in relation to its clustering of the melodies and how much this clustering
correlates with the clustering of the entire Agent population. A preparatory run is used
to evolve Agents using 30 melodies of known 'clustering'. The full run uses these
Agents as the initial population in evolving a new best Agent on a separate corpus of
melodies of random distance measures. This evolved Agent is then used in
combination with the original melody grammar to create a new melody which replaces
one of those from the initial random corpus. This results in a complex adaptive system
creating new melodies without any human input after initialisation. This paper
describes the behaviour of each phase in the system and presents a number of
melodies created by the system.

Title: EvoFashion: Customising Fashion Through Evolution
Authors: Nuno Lourenco, Filipe Assunção, Catarina Maçãs, and Penousal
Machado
Abstract: In todays society, where everyone desires unique and fashionable
products, the ability to customise products is almost mandatory in every online store.
Despite of many stores allowing the users to personalize their products, they do not
always do it in the most efficient and user-friendly manner. In order to have products
that reflect the user's design preferences, they have to go through a laborious process
of picking the components that they want to customise. In this paper we propose a
framework that aims to relieve the design burden from the user side, by automating
the design process through the use of Interactive Evolutionary Computation (IEC).
The framework is based on a web-interface that facilitates the interaction between the
user and the evolutionary process. The user can select between two types of
evolution: (i) automatic; and (ii) partially-automatic. The results show the ability of the
framework to promote evolution towards solutions that reflect the user aesthetic
preferences.

Title: A Swarm Environment for Experimental Performance and Improvisation
Authors: Frank Mauceri and Stephen M. Majercik
Abstract:This paper describes Swarm Performance and Improvisation (Swarm-PI), a
real-time computer environment for music improvisation that uses swarm algorithms to
control sound synthesis and to mediate interactions with a human performer. Swarm
models are artificial, multiagent systems where the organized movements of large
groups are the result of simple, local rules between individuals. Swarms typically
exhibit self-organization and emergent behavior. In Swarm-PI, multiple acoustic
descriptors from a live audio feed generate parameters for an independent swarm
among multiple swarms in the same space, and each swarm is used to synthesize a
stream of sound using granular sampling. This environment demonstrates the
effectiveness of using swarms to model human interactions typical to group
improvisation and to generate organized patterns of synthesized sound.

Title: Niche Constructing Drawing Robots
Authors: Jon McCormack
Abstract:This paper describes a series of experiments in creating autonomous
drawing robots that generate aesthetically interesting and engaging drawings. Based
on a previous method for multiple software agents that mimic the biological process of
niche construction, the challenge in this project was to re-interpret the implementation
of a set of evolving software agents into a physical robotic system. In this new robotic
system, individual robots try to reinforce a particular niche defined by the density of
the lines drawn underneath them. The paper also outlines the role of environmental
interactions in determining the style of drawing produced.

Title: Automated Shape Design by Grammatical Evolution
Authors: Manuel Muehlbauer, Jane Burry, and Andy Song
Abstract:This paper proposes a automated shape generation methodology based on
grammatical genetic programming for specific design cases. Two cases of the shape
generation are presented: architectural envelope design and facade design. Through
the described experiments, the applicability of this evolutionary method for design
applications is showcased. Through this study it can be seen that automated shape
generation by grammatical evolution offers a huge potential for the development of
performance-based creative systems.

Title: Evolutionary Image Transition Using Random Walks
Authors: Aneta Neumann, Bradley Alexander, Frank Neumann
Abstract:We present a study demonstrating how random walk algorithms can be
used for evolutionary image transition. We design different mutation operators based
on uniform and biased random walks and study how their combination with a baseline
mutation operator can lead to interesting image transition processes in terms of visual
effects and artistic features. Using feature-based analysis we investigate the
evolutionary image transition behaviour with respect to different features and evaluate
the images constructed during the image transition process.

Title: Evaluation Rules for Evolutionary Generation of Drum Patterns in Jazz Solos
Authors: Fabian Ostermann, Igor Vatolkin, Günter Rudolph
Abstract:The learning of improvisation in jazz and other music styles requires years
of practice. For music scholars which do not play in a band, technical solutions for
automatic generation of accompaniment on home computers are very helpful. They
may support the learning process and significantly improve the experience to play with
other musicians. However, many up-to-date approaches can not interact with a solo
player, generating static or random patterns without a direct musical dialogue between
a soloist and accompanying instruments. In this paper, we present a novel system for
the generation of drum patterns based on an evolutionary algorithm. As the main
extension to existing solutions, we propose a set of musically meaningful jazz-related
rules for the real-time validation and adjustment of generated drum patterns. In the
evaluation study, musicians agreed that the system can be successfully used for
learning of jazz improvisation and that the wide range of parameters helps to adapt
the response of the virtual drummer to the needs of individual scholars.

Title: Assessing Augmented Creativity: Putting a Lovelace Machine for Interactive
Title Generation through a Human Creativity Test
Authors: Yasser S. Arenas Rebolledo, Peter van der Putten, and Maarten H. Lamers
Abstract: The aim of this study is to find to what extent computers can assist humans
in the creative process of writing titles, using psychological tests for creativity that are
typically used for humans only . To this end, a computer tool was designed that
generates new titles to users, based on knowledge generated from a pre-built corpus.
This paper gives a description of both the development of the system as well as tests
applied to the participants, derived from classical psycho- logical tests for human
creativity. A total of 89 participants divided in two groups completed two tasks which
consisted of generating titles for paintings. One group was allowed to use a template-
based system for generating titles, the other group did not use any tools. The results
of the experiments show higher creativity scores for the combination of participants
augmented by a computational creativity tool.

Title: Play it Again: Evolved Audio Effects and Synthesizer Programming
Authors: Benjamin D. Smith
Abstract:Automatic programming of sound synthesizers and audio devices to match a
given, desired sound is examined and a Genetic Algorithm (GA) that functions
independent of specific synthesis techniques is proposed. Most work in this area has
focused on one synthesis model or synthesizer, designing the GA and tuning the
operator parameters to obtain optimal results. The scope of such inquiries has been
limited by available computing power, however current software (Ableton Live, herein)
and commercially available hardware is shown to quickly find accurate solutions,
promising a practical application for music creators. Both software synthesizers and
audio effects processors are examined, showing a wide range of performance times
(from seconds to hours) and solution accuracy, based on particularities of the target
devices. Random oscillators, phase synchronizing, and filters over empty frequency
ranges are identified as primary challenges for GA based optimization.

Title: Fashion Design Aid System with Application of Interactive Genetic Algorithms
Authors: Nazanin Alsadat Tabatabaei Anaraki
Abstract:These days, consumers can make their choice from a wide variety of clothes
provided in the market; however, some prefer to have their clothes custom-made.
Since most of these consumers are not professional designers, they contact a
designer to help them with the process. This approach, however, is not efficient in
terms of time and cost and it does not reflect the consumer's personal taste as much
as desired. This study proposes a design system using Interactive Genetic Algorithm
(IGA) to overcome these problems. IGA differs from traditional Genetic Algorithm (GA)
by leaving the fitness function to the personal preference of the user. The proposed
system uses user's taste as a fitness value to create a large number of design
options, and it is based on an encoding scheme either describing a dress as a whole
or as a two-part piece of clothing. The system is designed in the Rhinoceros 3D
software, using python, which provides good speed and interface options. The
assessment experiments with several subjects indicated that the proposed system is
effective.

Title: Generalisation Performance of Western Instrument Recognition Models in
Polyphonic Mixtures with Ethnic Samples
Authors: Igor Vatolkin
Abstract: Instrument recognition in polyphonic audio recordings is a very complex
task. Most research studies until now were focussed on the recognition of Western
instruments in Western classical and popular music, but also an increasing number of
recent works addressed the classification of ethnic/world recordings. However, such
studies are typically restricted to one kind of music and do not measure the bias of
"Western" effect, i.e., the danger of overfitting towards Western music when the
classification models are optimised only for such tracks. In this paper, we analyse the
performance of several instrument classification models which are trained and
optimised on polyphonic mixtures of Western instruments, but independently validated
on mixtures created with randomly added ethnic samples. The conducted experiments
include evolutionary multi-objective feature selection from a large set of audio signal
descriptors and the estimation of individual feature relevance.

Title: Exploring the Exactitudes Portrait Series with Restricted Boltzmann Machines
Authors: Sam D. Verkoelen, Maarten H. Lamers, Peter van der Putten
Abstract: In this paper we explore the use of deep neural networks to analyze semi-
structured series of artworks. We train stacked Restricted Boltzmann Machines on the
Exactitudes collection of photo series, and use this to understand the relationship
between works and series, uncover underlying features and dimensions, and
generate new images. The projection of the series on the two major decorrelated
features (PCA on top of Boltzmann features) results in a visualization that clearly
reflects the semi structured nature of the photos series, although the original features
provide better classification results when assigning photographs to series. This work
provides a useful case example of understanding structure that is uncovered by deep
neural networks, as well as a tool to analyze the underlying structure of a collection of
visual artworks, as a very first step towards a robot curator.

Title: Evolving Mondrian-Style Artworks
Authors: Miri Weiss Cohen, Leticia Cherchiglia, Rachel Costa
Abstract: This paper describes a Genetic Algorithm (GA) software system for
automatically generating Mondrian-style symmetries and abstract artwork. The
research examines Mondrian's paintings from 1922 through 1932 and analyses the
balances, color symmetries and composition in these paintings. We used a set of
eleven criteria to define the automated system. We then translated and formulized
these criteria into heuristics and criteria that can be measured and used in the GA
algorithm. The software includes a module that provides a range of GA parameter
values for interactive selection. Despite a number of limitations, the method yielded
high quality results with colors close to those of Mondrian and rectangles that did not
overlap and fit the canvas.

Title: Predicting Expressive Bow Controls for Violin and Viola
Authors: Lauren Jane Yu and Andrea Pohoreckyj Danyluk
Abstract: Though computational systems can simulate notes on a staff of sheet
music, capturing the artistic liberties professional musicians take to communicate their
interpretation of those notes is a much more difficult task. In this paper, we
demonstrate that machine learning methods can be used to learn models of
expressivity, focusing on bow articulation for violin and viola. First we describe a new
data set of annotated sheet music with information about specific aspects of bow
control. We then present experiments for building and testing predictive models for
these bow controls, as well as analysis that includes both general metrics and manual
examination.

Publication Details

Submissions will be rigorously reviewed for scientific and artistic merit. Accepted papers will be presented orally or as posters at the event and included in the evostar proceedings, published by Springer Verlag in a dedicated volume of the Lecture Notes in Computer Science series. The acceptance rate at evoMUSART 2016 was 40% for papers accepted for oral presentation, and 24% for poster presentation.

Submitters are strongly encouraged to provide in all papers a link for download of media demonstrating their results, whether music, images, video, or other media types. Links should be anonymised for double-blind review, e.g. using a URL shortening service.